You might find it helpful to look at the sim.hmm() function in the
"hmm.discnp" package, or the simHMM() function in the "HMM" package.
cheers,
Rolf Turner
On 03/06/14 21:17, Bukar Alhaji wrote:
Dear R buddies,
Sorry for this silly question but am new to R. I am trying to
generate states and observations to be use for Bayesian Hidden Markov
Models analysis where i intend using mixture of Poisson and Negative
binomial as emulsion. I use the code below to generate states and
observations for homogeneous HMM . I would like to know if i
correctly generated the data.
pii = c(0.6,0.4)
p1 <- matrix(c(0.8,0.2,0.3,0.7),byrow=TRUE,nrow=2)
NUM = 200
theta<-rep(0, NUM)
x<-rep(0, NUM)
## generating the states
# initial state
theta[1]<-rbinom(1, 1, pii[1])
# other states
for (i in 2:NUM)
{
if (theta[i-1]==0)
theta[i]<-rbinom(1, 1, p1[1, 1])
else
theta[i]<-rbinom(1, 1, p1[2, 1])
}
## generating the observations
for (i in 1:NUM)
{
if (theta[i]==0)
{
x[i]<-rpois(1, 5)
}
else
{
x[i]<-rnbinom(1, 3, 0.3)
}
}
data<-list(s=theta, o=x, p1 = p1, pii = pii)
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